The Role
Develops and maintains scalable data pipelines, data lakes, lakehouse solutions, storage architectures, and cloud-based data platforms. Ensures data quality, governance, security, testing, monitoring, and integrity while supporting analytics and data-driven applications. Collaborates with business, IT, analysts, and data scientists to translate requirements into reliable solutions using Azure, Databricks, Python, Scala, SQL, and distributed processing technologies. Documents technical solutions and contributes to Agile delivery and continuous improvement.
Summary Generated by Built In
The Data Engineer supports, develops, and maintains data and analytics platforms that enable reliable, scalable, and efficient access to data. This role partners with Business and IT teams to understand requirements and leverage modern data engineering technologies to deliver high-quality data solutions at scale. The Data Engineer will design, develop, and maintain data pipelines and data storage solutions, ensure data quality and integrity, and contribute to data governance, analytics, and cloud-based data platforms. The role works within Agile delivery environments and applies modern engineering practices to continuously improve data solutions.
Key Responsibilities- Develop and maintain reliable, scalable, and efficient ETL/ELT data pipelines using technologies such as Azure Databricks, PySpark, Python/Scala, and SQL.
- Design and implement data ingestion and transformation solutions for a variety of data sources, including relational, event-based, and unstructured data.
- Build and maintain scalable Data Lake and Lakehouse solutions and optimize data processing and storage performance.
- Develop physical data models and implement data storage architectures in accordance with established design and engineering guidelines.
- Implement data quality checks, monitoring, alerting, and troubleshooting mechanisms to identify and resolve data quality and data integrity issues.
- Analyze complex data elements, data flows, dependencies, and relationships to contribute to conceptual, logical, and physical data models.
- Develop and operate large-scale data storage and processing solutions using distributed and cloud-based technologies.
- Work with Azure services such as Azure Data Lake Storage (ADLS), Event Hubs, and Azure Functions to support scalable data solutions.
- Implement and support data governance practices, including metadata management, data access, retention, and availability.
- Participate in testing, validation, troubleshooting, and continuous improvement of data pipelines and solutions.
- Collaborate with business stakeholders, analysts, data scientists, and IT teams to understand requirements and deliver analytics and data solutions.
- Apply Agile development practices, including Scrum, Kanban, DevOps, and continuous improvement, to deliver data-driven solutions.
- Document technical solutions, processes, data flows, and system dependencies to support knowledge sharing and effective solution maintenance.
- Apply appropriate engineering, security, governance, and compliance practices throughout the data development lifecycle.
- Strong programming skills in Python/PySpark or Scala, with the ability to develop, test, and maintain production-quality code.
- Strong knowledge of SQL and experience working with data extraction, transformation, and loading processes.
- Hands-on experience with Azure Databricks and cloud-based data engineering solutions.
- Experience with Azure data services, including ADLS, Event Hub, and Azure Functions.
- Good understanding of Data Lake, Delta Lake, Lakehouse, ETL/ELT, data modeling, and distributed data processing concepts.
- Exposure to or knowledge of Big Data technologies such as Spark, MapReduce, Hive, HBase, Kafka, or equivalent technologies.
- Experience or exposure to clustered compute and cloud-based implementations.
- Familiarity with designing solutions that support large-scale data movement and processing in cloud environments.
- Understanding of data quality, data integrity, metadata, governance, and data management principles.
- Exposure to analytical solutions, IoT technologies, and data-driven applications is beneficial.
- Familiarity with Agile software development, DevOps, Scrum, or Kanban methodologies.
- Strong problem-solving and analytical skills, with the ability to identify root causes and implement robust solutions.
- Strong communication and collaboration skills, with the ability to work effectively with technical and non-technical stakeholders.
- Customer-focused mindset with the ability to understand stakeholder needs and translate them into effective technical solutions.
- Ability to document solutions clearly and communicate technical information to different audiences.
- Strong focus on quality, solution validation, testing, security, governance, and continuous improvement.
- Ability to work collaboratively in diverse teams and recognize the value of different perspectives.
- System Requirements Engineering
- Data Extraction and ETL/ELT
- Programming and Data Engineering
- Data Quality and Data Governance
- Solution Validation and Testing
- Problem Solving and Decision Quality
- Quality Assurance and Metrics
- Solution Documentation
- Customer Focus
- Collaboration and Effective Communication
- Continuous Improvement
- Bachelor's degree or equivalent qualification in Computer Science, Information Technology, Engineering, Data Science, or another relevant technical discipline, or equivalent relevant experience.
- 2–4 years of relevant Data Engineering experience preferred.
- Relevant experience through internships, co-op programs, temporary student employment, academic projects, or extracurricular technical activities may be considered for early-career candidates.
- Relevant certifications such as Databricks or Azure certifications are an advantage.
- This position may require licensing or compliance with applicable export control or sanctions regulations.
Skills Required
- Bachelor's degree or equivalent qualification in Computer Science, Information Technology, Engineering, Data Science, or another relevant technical discipline, or equivalent relevant experience.
- 2–4 years of relevant Data Engineering experience.
- Strong programming skills in Python/PySpark or Scala.
- Strong knowledge of SQL and ETL/ELT processes.
- Hands-on experience with Azure Databricks and cloud-based data engineering solutions.
- Experience with Azure Data Lake Storage, Event Hubs, and Azure Functions.
- Knowledge of Data Lake, Delta Lake, Lakehouse, data modeling, and distributed data processing.
- Exposure to Big Data technologies such as Spark, MapReduce, Hive, HBase, Kafka, or equivalent technologies.
- Experience or exposure to clustered compute and cloud-based implementations.
- Understanding of data quality, data integrity, metadata, governance, and data management principles.
- Familiarity with Agile software development, DevOps, Scrum, or Kanban methodologies.
- Relevant Databricks or Azure certifications.
- May require licensing or compliance with applicable export control or sanctions regulations.
Cummins Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Cummins and has not been reviewed or approved by Cummins.
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Retirement Support — A 401(k) with company contribution/match and both defined contribution and defined benefit pension plans are offered, alongside profit sharing and an employee stock purchase plan. This mix supports long-term savings and financial security.
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Healthcare Strength — Multiple medical plan options (HSA, HSA Plus, PPO) with dental, vision, life and long-term disability coverage are provided, along with telehealth, mental-health support, and wellness tools. In-network protections and HSA/HSA Plus structures are described to help manage costs.
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Parental & Family Support — Paid maternity and paternity leave, family medical leave, and adoption assistance are offered. Reduced or flexible hours and unpaid extended leave options further support caregiving needs.
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The Company
What We Do
At Cummins, we empower everyone to grow their careers through meaningful work, building inclusive and equitable teams, coaching, development and opportunities to make a difference. Across our entire organization, you'll find engineers, developers, and technicians who are innovating, designing, testing, and building. You'll also find accountants, marketers, as well as manufacturing, quality and supply chain specialists who are working with technology that's just as innovative and advanced.
